Deep Convolution Neural Networks (Deep CNNs) are the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve the problem of character recognition, but their use has become as widespread as it is now thanks to recent research. These deep CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolution neural network became a very useful tool in the field of the machine learning.
The CNN may include a feature extractor that extracts features from an image and a feature classifier that recognizes the image by referring to the features extracted from the feature extractor or detects objects in the image.
According to a conventional CNN, a plurality of blocks including convolutional layers are used to extract features from an image. For example, each of the blocks may apply a 3×3 convolution operation to the image or its corresponding feature map by using a filter kernel having a size of 3×3, to thereby extract the features from the image.
However, according to the convolution operation of the conventional CNN, if it is assumed that the size of the input image is (X, Y), the number of channels of the input image is Z, the size of the filter kernel is (M, M), and the number of filters is L, the amount of computation becomes XYZM2L, the number of parameters becomes L(ZM2+1), and the amount of computation and the number of parameters increase due to many factors, such as the number of channels, the number of filters, and the kernel size. In order to prevent the degradation of computing performance due to the increase in the amount of computation, a sub-sampled feature map, e.g., a feature map whose size is reduced in comparison with a size of the input image, may be used. However, since the size of the feature map is reduced by such a sub-sampling, a performance of extracting the features for image recognition or object detection may be deteriorated.
Therefore, a method for extracting the features accurately while reducing the amount of computation is proposed.